Abstract
Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.
Original language | English |
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Title of host publication | Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Proceedings |
Editors | Joemon M. Jose, Emine Yilmaz, João Magalhães, Flávio Martins, Pablo Castells, Nicola Ferro, Mário J. Silva |
Publisher | Springer |
Pages | 35-42 |
Number of pages | 8 |
ISBN (Print) | 9783030454418 |
DOIs | |
Publication status | Published - 1 Jan 2020 |
Event | 42nd European Conference on IR Research: ECIR 2020 - Virtuell, Lisbon, Portugal Duration: 14 Apr 2020 → 17 Apr 2020 https://ecir2020.org/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12036 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 42nd European Conference on IR Research |
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Country/Territory | Portugal |
City | Virtuell, Lisbon |
Period | 14/04/20 → 17/04/20 |
Internet address |
Keywords
- Algorithmic fairness
- Item popularity
- Music recommendation
- Popularity bias
- Recommender systems
- Reproducibility
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science